# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# BSD 3-Clause License
#
# Copyright (c) 2017 xxxx
# All rights reserved.
# Copyright 2021 Huawei Technologies Co., Ltd
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright notice, this
#   list of conditions and the following disclaimer.
#
# * Redistributions in binary form must reproduce the above copyright notice,
#   this list of conditions and the following disclaimer in the documentation
#   and/or other materials provided with the distribution.
#
# * Neither the name of the copyright holder nor the names of its
#   contributors may be used to endorse or promote products derived from
#   this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
# ============================================================================
#
# ==============================================================================

"""Numpy BoxMaskList classes and functions."""

from __future__ import (
    absolute_import,
    division,
    print_function,
    unicode_literals,
)
import numpy as np

from . import np_box_list
import os
NPU_CALCULATE_DEVICE = 0
if os.getenv('NPU_CALCULATE_DEVICE') and str.isdigit(os.getenv('NPU_CALCULATE_DEVICE')):
    NPU_CALCULATE_DEVICE = int(os.getenv('NPU_CALCULATE_DEVICE'))


class BoxMaskList(np_box_list.BoxList):
    """Convenience wrapper for BoxList with masks.

  BoxMaskList extends the np_box_list.BoxList to contain masks as well.
  In particular, its constructor receives both boxes and masks. Note that the
  masks correspond to the full image.
  """

    def __init__(self, box_data, mask_data):
        """Constructs box collection.

    Args:
      box_data: a numpy array of shape [N, 4] representing box coordinates
      mask_data: a numpy array of shape [N, height, width] representing masks
        with values are in {0,1}. The masks correspond to the full
        image. The height and the width will be equal to image height and width.

    Raises:
      ValueError: if bbox data is not a numpy array
      ValueError: if invalid dimensions for bbox data
      ValueError: if mask data is not a numpy array
      ValueError: if invalid dimension for mask data
    """
        super(BoxMaskList, self).__init__(box_data)
        if not isinstance(mask_data, np.ndarray):
            raise ValueError("Mask data must be a numpy array.")
        if len(mask_data.shape) != 3:
            raise ValueError("Invalid dimensions for mask data.")
        if mask_data.dtype != np.uint8:
            raise ValueError(
                "Invalid data type for mask data: uint8 is required."
            )
        if mask_data.shape[0] != box_data.shape[0]:
            raise ValueError(
                "There should be the same number of boxes and masks."
            )
        self.data["masks"] = mask_data

    def get_masks(self):
        """Convenience function for accessing masks.

    Returns:
      a numpy array of shape [N, height, width] representing masks
    """
        return self.get_field("masks")
